class_docstr: An implementation of `"LDP" <https://arxiv.org/abs/1811.03508>`_ from the
  ICLR Representation Learning on Graphs and Manifolds Workshop '19 paper "A
  Simple Yet Effective Baseline for Non-Attributed Graph Classification". The
  procedure calculates histograms of degree profiles. These concatenated
  histograms form the graph representations.
  
  Args:
      bins (int): Number of histogram bins. Default is 32.
docstr: Fitting an LDP model.
  
  Arg types:
      * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
docstr_tok: ["Fitting","an","LDP","model",".","Arg","types",":","*","*","*","graphs","*","*","*","(","List","of","NetworkX","graphs",")","*","-","The","graphs","to","be","embedded","."]
code: def fit(self, graphs):
      """
      Fitting an LDP model.
  
      Arg types:
          * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
      """
      graphs = self._check_graphs(graphs)
      self._embedding = [self._calculate_ldp(graph) for graph in graphs]
  
file_path: karateclub/karateclub/graph_embedding/ldp.py
imports: import numpy
  import networkx
  from karateclub.estimator import Estimator
  from typing import List
label_desc: Use image node_id 3 for calling the LDP obj's underlying member method code with example usage: obj.fit(graphs) without return types